OpenRaiser/Intern-Atlas huggingface dataset:
How to Databasify a Blockchain: the Case of Hyperledger Fabric
Venue: arXiv cs.DC 2018
Year: 2018

Within the last few years, a countless number of blockchain systems have emerged on the market, each one claiming to revolutionize the way of distributed transaction processing in one way or the other. Many blockchain features, such as byzantine fault tolerance (BFT), are indeed valuable additions in modern environments. However, despite all the hype around the technology, many of the challenges that blockchain systems have to face are fundamental transaction management problems. These are largely shared with traditional database systems, which have been around for decades already. These similarities become especially visible for systems, that blur the lines between blockchain systems and classical database systems. A great example of this is Hyperledger Fabric, an open-source permissioned blockchain system under development by IBM. By having a relaxed view on BFT, the transaction pipeline of Fabric highly resembles the workflow of classical distributed databases systems. This raises two questions: (1) Which conceptual similarities and differences do actually exist between a system such as Fabric and a classical distributed database system? (2) Is it possible to improve on the performance of Fabric by transitioning technology from the database world to blockchains and thus blurring the lines between these two types of systems even further? To tackle these questions, we first explore Fabric from the perspective of database research, where we observe weaknesses in the transaction pipeline. We then solve these issues by transitioning well-understood database concepts to Fabric, namely transaction reordering as well as early transaction abort. Our experimental evaluation shows that our improved version Fabric++ significantly increases the throughput of successful transactions over the vanilla version by up to a factor of 3x.
A multi-agent system for managing the product lifecycle sustainability
Venue: arXiv cs.MA 2018
Year: 2018

The international competitive market causes the increasing of shorten product life cycle and product development process with the improvement in term of time, cost and quality while increasing the waste generation. Product life cycle sustainability can reduce waste, conserve resources, use recycling materials, design product for easy disassembly and avoid using hazardous material. This paper proposes a knowledge management architecture, based on a multi-agent system, which focuses on the "sustainability" in order to manage knowledge in each stage of the product lifecycle, and particularly in the recovery process. The aim of this research work is to make the link between a decision-making system based on the agent's knowledge about the sustainability (environmental norms, rules...) and a PLM (Product Lifecycle Management) system. The software Agents will help the decision makers in each stage of the lifecycle and make them take into account the environmental impact of their decisions.
When research assessment exercises leave room for opportunistic behavior by the subjects under evaluation
Venue: arXiv cs.DL 2018
Year: 2018

This study inserts in the stream of research on the perverse effects that PBRF systems can induce in the subjects evaluated. The authors' opinion is that more often than not, it is the doubtful scientific basis of the evaluation criteria that leave room for opportunistic behaviors. The work examines the 2004-2010 Italian national research assessment (VQR) to verify possible opportunistic behavior by universities in order to limit the penalization of their performance (and funding) due to the presence of scientifically unproductive professors in faculty. In particular, institutions may have favored "gift authorship" practices. The analysis thus focuses on the output of professors who were unproductive in the VQR publication window, but became productive ("new productives") in the following five years: a number of universities show a remarkably higher than average share of publications by new productives that are in co-authorship exclusively with colleagues from the same university.
How important is choice of the scaling factor in standardizing citations?
Venue: arXiv cs.DL 2018
Year: 2018

Because of the variations in citation behavior across research fields, appropriate standardization must be applied as part of any bibliometric analysis of the productivity of individual scientists and research organizations. Such standardization involves scaling by some factor that characterizes the distribution of the citations of articles from the same year and subject category. In this work we conduct an analysis of the sensitivity of researchers' productivity rankings to the scaling factor chosen to standardize their citations. To do this we first prepare the productivity rankings for all researchers (more than 30,000) operating in the hard sciences in Italy, over the period 2004-2008. We then measure the shifts in rankings caused by adopting scaling factors other than the particular factor that seems more effective for comparing the impact of publications in different fields: the citation average of the distribution of cited-only publications.
Generation of random chordal graphs using subtrees of a tree
Venue: arXiv cs.DM 2018
Year: 2018

Chordal graphs form one of the most studied graph classes. Several graph problems that are NP-hard in general become solvable in polynomial time on chordal graphs, whereas many others remain NP-hard. For a large group of problems among the latter, approximation algorithms, parameterized algorithms, and algorithms with moderately exponential or sub-exponential running time have been designed. Chordal graphs have also gained increasing interest during the recent years in the area of enumeration algorithms. Being able to test these algorithms on instances of chordal graphs is crucial for understanding the concepts of tractability of hard problems on graph classes. Unfortunately, only few studies give algorithms for generating chordal graphs. Even in these papers, only very few methods aim for generating a large variety of chordal graphs. Surprisingly, none of these methods is directly based on the "intersection of subtrees of a tree" characterization of chordal graphs. In this paper, we give an algorithm for generating chordal graphs, based on the characterization that a graph is chordal if and only if it is the intersection graph of subtrees of a tree. Upon generating a random host tree, we give and test various methods that generate subtrees of the host tree. We compare our methods to one another and to existing ones for generating chordal graphs. Our experiments show that one of our methods is able to generate the largest variety of chordal graphs in terms of maximal clique sizes. Moreover, two of our subtree generation methods result in an overall complexity of our generation algorithm that is the best possible time complexity for a method generating the entire node set of subtrees in a "intersection of subtrees of a tree" representation. The instances corresponding to the results presented in this paper, and also a set of relatively small-sized instances are made available online.
A methodology to compute the territorial productivity of scientists: The case of Italy
Venue: arXiv cs.DL 2018
Year: 2018

Policy-makers working at the national and regional levels could find the territorial mapping of research productivity by field to be useful in informing both research and industrial policy. Research-based private companies could also use such mapping for efficient selection in localizing R&D activities and university research collaborations. In this work we apply a bibliometric methodology for ranking by research productivity: i) the fields of research for each territory (region and province); and ii) the territories for each scientific field. The analysis is based on the 2008-2012 scientific output indexed in the Web of Science, by all professors on staff at Italian universities. The population is over 36,000 professors, active in 192 fields and 9 disciplines.
A new approach to measure the scientific strengths of territories
Venue: arXiv cs.DL 2018
Year: 2018

The current work applies a methodology for mapping the supply of new knowledge from public research organizations, in this case from Italian institutions at the level of regions and provinces (NUTS2 and NUTS3). Through the analysis of scientific production indexed in the Web of Science for the period 2006-2010, the new knowledge is classified in subject categories and mapped according to an algorithm for the reconciliation of authors' affiliations. Unlike other studies in the literature based on simple counting of publications, the present study adopts an indicator, Scientific Strength, which takes account of both the quantity of scientific production and its impact on the advancement of knowledge. The differences in the results that arise from the two approaches are examined. The results of works of this kind can inform public research policies, at national and local levels, as well as the localization strategies of research-based companies.
Gender differences in research collaboration
Venue: arXiv cs.DL 2018
Year: 2018

The debate on the role of women in the academic world has focused on various phenomena that could be at the root of the gender gap seen in many nations. However, in spite of the ever more collaborative character of scientific research, the issue of gender aspects in research collaborations has been treated in a marginal manner. In this article we apply an innovative bibliometric approach based on the propensity for collaboration by individual academics, which permits measurement of gender differences in the propensity to collaborate by fields, disciplines and forms of collaboration: intramural, extramural domestic and international. The analysis of the scientific production of Italian academics shows that women researchers register a greater capacity to collaborate in all the forms analyzed, with the exception of international collaboration, where there is still a gap in comparison to male colleagues.
You May Not Need Attention
Venue: arXiv cs.CL 2018
Year: 2018

In NMT, how far can we get without attention and without separate encoding and decoding? To answer that question, we introduce a recurrent neural translation model that does not use attention and does not have a separate encoder and decoder. Our eager translation model is low-latency, writing target tokens as soon as it reads the first source token, and uses constant memory during decoding. It performs on par with the standard attention-based model of Bahdanau et al. (2014), and better on long sentences.
Generating Texts with Integer Linear Programming
Venue: arXiv cs.CL 2018
Year: 2018

Concept-to-text generation typically employs a pipeline architecture, which often leads to suboptimal texts. Content selection, for example, may greedily select the most important facts, which may require, however, too many words to express, and this may be undesirable when space is limited or expensive. Selecting other facts, possibly only slightly less important, may allow the lexicalization stage to use much fewer words, or to report more facts in the same space. Decisions made during content selection and lexicalization may also lead to more or fewer sentence aggregation opportunities, affecting the length and readability of the resulting texts. Building upon on a publicly available state of the art natural language generator for Semantic Web ontologies, this article presents an Integer Linear Programming model that, unlike pipeline architectures, jointly considers choices available in content selection, lexicalization, and sentence aggregation to avoid greedy local decisions and produce more compact texts, i.e., texts that report more facts per word. Compact texts are desirable, for example, when generating advertisements to be included in Web search results, or when summarizing structured information in limited space. An extended version of the proposed model also considers a limited form of referring expression generation and avoids redundant sentences. An approximation of the two models can be used when longer texts need to be generated. Experiments with three ontologies confirm that the proposed models lead to more compact texts, compared to pipeline systems, with no deterioration or with improvements in the perceived quality of the generated texts.
DEEPGONET: Multi-label Prediction of GO Annotation for Protein from Sequence Using Cascaded Convolutional and Recurrent Network
Venue: arXiv cs.CV 2018
Year: 2018

The present gap between the amount of available protein sequence due to the development of next generation sequencing technology (NGS) and slow and expensive experimental extraction of useful information like annotation of protein sequence in different functional aspects, is ever widening, which can be reduced by employing automatic function prediction (AFP) approaches. Gene Ontology (GO), comprising of more than 40, 000 classes, defines three aspects of protein function names Biological Process (BP), Cellular Component (CC), Molecular Function (MF). Multiple functions of a single protein, has made automatic function prediction a large-scale, multi-class, multi-label task. In this paper, we present DEEPGONET, a novel cascaded convolutional and recurrent neural network, to predict the top-level hierarchy of GO ontology. The network takes the primary sequence of protein as input which makes it more useful than other prevailing state-of-the-art deep learning based methods with multi-modal input, making them less applicable for proteins where only primary sequence is available. All predictions of different protein functions of our network are performed by the same architecture, a proof of better generalization as demonstrated by promising performance on a variety of organisms while trained on Homo sapiens only, which is made possible by efficient exploration of vast output space by leveraging hierarchical relationship among GO classes. The promising performance of our model makes it a potential avenue for directing experimental protein functions exploration efficiently by vastly eliminating possible routes which is done by the exploring only the suggested routes from our model. Our proposed model is also very simple and efficient in terms of computational time and space compared to other architectures in literature.
A Mixture of Expert Approach for Low-Cost Customization of Deep Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

The ability to customize a trained Deep Neural Network (DNN) locally using user-specific data may greatly enhance user experiences, reduce development costs, and protect user's privacy. In this work, we propose to incorporate a novel Mixture of Experts (MOE) approach to accomplish this goal. This architecture comprises of a Global Expert (GE), a Local Expert (LE) and a Gating Network (GN). The GE is a trained DNN developed on a large training dataset representative of many potential users. After deployment on an embedded edge device, GE will be subject to customized, user-specific data (e.g., accent in speech) and its performance may suffer. This problem may be alleviated by training a local DNN (the local expert, LE) on a small size customized training data to correct the errors made by GE. A gating network then will be trained to determine whether an incoming data should be handled by GE or LE. Since the customized dataset is in general very small, the cost of training LE and GN would be much lower than that of re-training of GE. The training of LE and GN thus can be performed at local device, properly protecting the privacy of customized training data. In this work, we developed a prototype MOE architecture for handwritten alphanumeric character recognition task. We use EMNIST as the generic dataset, LeNet5 as GE, and handwritings of 10 users as the customized dataset. We show that with the LE and GN, the classification accuracy is significantly enhanced over the customized dataset with almost no degradation of accuracy over the generic dataset. In terms of energy and network size, the overhead of LE and GN is around 2.5% compared to those of GE.
A Process-driven View on Summative Evaluation of Visual Analytics Solutions
Venue: arXiv cs.HC 2018
Year: 2018

Many evaluation methods have been applied to assess the usefulness of visual analytics solutions. These methods are branching from a variety of origins with different assumptions, and goals. We provide a high-level overview of the process employed in each method using the generic evaluation model "GEM" that generalizes the process of usefulness evaluation. The model treats evaluation methods as processes that generate evidence of usefulness as output. Our model serves three purposes: It educate new VA practitioners about the heterogeneous evaluation practices in the field, it highlights potential risks in the process of evaluation which reduces their validity and It provide a guideline to elect suitable evaluation method.
Face Recognition: From Traditional to Deep Learning Methods
Venue: arXiv cs.CV 2018
Year: 2018

Starting in the seventies, face recognition has become one of the most researched topics in computer vision and biometrics. Traditional methods based on hand-crafted features and traditional machine learning techniques have recently been superseded by deep neural networks trained with very large datasets. In this paper we provide a comprehensive and up-to-date literature review of popular face recognition methods including both traditional (geometry-based, holistic, feature-based and hybrid methods) and deep learning methods.
A Mixture Model Based Defense for Data Poisoning Attacks Against Naive Bayes Spam Filters
Venue: arXiv cs.CR 2018
Year: 2018

Naive Bayes spam filters are highly susceptible to data poisoning attacks. Here, known spam sources/blacklisted IPs exploit the fact that their received emails will be treated as (ground truth) labeled spam examples, and used for classifier training (or re-training). The attacking source thus generates emails that will skew the spam model, potentially resulting in great degradation in classifier accuracy. Such attacks are successful mainly because of the poor representation power of the naive Bayes (NB) model, with only a single (component) density to represent spam (plus a possible attack). We propose a defense based on the use of a mixture of NB models. We demonstrate that the learned mixture almost completely isolates the attack in a second NB component, with the original spam component essentially unchanged by the attack. Our approach addresses both the scenario where the classifier is being re-trained in light of new data and, significantly, the more challenging scenario where the attack is embedded in the original spam training set. Even for weak attack strengths, BIC-based model order selection chooses a two-component solution, which invokes the mixture-based defense. Promising results are presented on the TREC 2005 spam corpus.
Measuring Issue Ownership using Word Embeddings
Venue: arXiv cs.CL 2018
Year: 2018

Sentiment and topic analysis are common methods used for social media monitoring. Essentially, these methods answers questions such as, "what is being talked about, regarding X", and "what do people feel, regarding X". In this paper, we investigate another venue for social media monitoring, namely issue ownership and agenda setting, which are concepts from political science that have been used to explain voter choice and electoral outcomes. We argue that issue alignment and agenda setting can be seen as a kind of semantic source similarity of the kind "how similar is source A to issue owner P, when talking about issue X", and as such can be measured using word/document embedding techniques. We present work in progress towards measuring that kind of conditioned similarity, and introduce a new notion of similarity for predictive embeddings. We then test this method by measuring the similarity between politically aligned media and political parties, conditioned on bloc-specific issues.
Towards a Simple Approach to Multi-step Model-based Reinforcement Learning
Venue: arXiv cs.LG 2018
Year: 2018

When environmental interaction is expensive, model-based reinforcement learning offers a solution by planning ahead and avoiding costly mistakes. Model-based agents typically learn a single-step transition model. In this paper, we propose a multi-step model that predicts the outcome of an action sequence with variable length. We show that this model is easy to learn, and that the model can make policy-conditional predictions. We report preliminary results that show a clear advantage for the multi-step model compared to its one-step counterpart.
OpenCL Performance Prediction using Architecture-Independent Features
Venue: arXiv cs.DC 2018
Year: 2018

OpenCL is an attractive model for heterogeneous high-performance computing systems, with wide support from hardware vendors and significant performance portability. To support efficient scheduling on HPC systems it is necessary to perform accurate performance predictions for OpenCL workloads on varied compute devices, which is challenging due to diverse computation, communication and memory access characteristics which result in varying performance between devices. The Architecture Independent Workload Characterization (AIWC) tool can be used to characterize OpenCL kernels according to a set of architecture-independent features. This work presents a methodology where AIWC features are used to form a model capable of predicting accelerator execution times. We used this methodology to predict execution times for a set of 37 computational kernels running on 15 different devices representing a broad range of CPU, GPU and MIC architectures. The predictions are highly accurate, differing from the measured experimental run-times by an average of only 1.2%, and correspond to actual execution time mispredictions of 9 μs to 1 sec according to problem size. A previously unencountered code can be instrumented once and the AIWC metrics embedded in the kernel, to allow performance prediction across the full range of modelled devices. The results suggest that this methodology supports correct selection of the most appropriate device for a previously unencountered code, which is highly relevant to the HPC scheduling setting.
PerceptionNet: A Deep Convolutional Neural Network for Late Sensor Fusion
Venue: arXiv cs.LG 2018
Year: 2018

Human Activity Recognition (HAR) based on motion sensors has drawn a lot of attention over the last few years, since perceiving the human status enables context-aware applications to adapt their services on users' needs. However, motion sensor fusion and feature extraction have not reached their full potentials, remaining still an open issue. In this paper, we introduce PerceptionNet, a deep Convolutional Neural Network (CNN) that applies a late 2D convolution to multimodal time-series sensor data, in order to extract automatically efficient features for HAR. We evaluate our approach on two public available HAR datasets to demonstrate that the proposed model fuses effectively multimodal sensors and improves the performance of HAR. In particular, PerceptionNet surpasses the performance of state-of-the-art HAR methods based on: (i) features extracted from humans, (ii) deep CNNs exploiting early fusion approaches, and (iii) Long Short-Term Memory (LSTM), by an average accuracy of more than 3%.